Deep-learning based methods have been previously used to generate synthetic CTs from MR images[1]}. Dual-echo ultrashort TE (UTE) MR imaging[2]} was used to train a 2D U-Net[3]} that was efficient at generating realistic skulls, but UTE scans are not widely available and require development on an MR scanner, as they are not standard protocols. An alternative to UTE images are T1-weighted images, but these can be more challenging to synthesize CT skulls from than UTE because UTE imaging can capture signals from tissues with a very short transverse relaxation time such as bone, providing more information for skull synthesis. For instance, Lei et al.[4]} proposed to use patch-based features extracted from MRIs to train a sequence of alternating random forests based on an iterative refinement model. Maspero et al.[5]} trained three 2D cGANs[6]} for each plane and combine the results to generate synthetic CT from T1-weighted MRI. Gupta et al.[7]} proposed to train a 2D U-Net on sagittal views of MRIs and synthesize the HU of air, soft tissue and bone in three output channels. However, 2D networks are limited by the lack of information of relationship between slices[8]} and the skulls are not spatially continuous (i.e generated volumes can appear jagged) along the views that are not involved in training. The irregular skull geometry of sCT may lead to significant differences in tFUS planning. In very recent work done in parallel to ours [9]} a 3D cGAN was proposed to synthesize the whole head CT from MR images. Here, we focus on the skull, which is the critical structure for tFUS.
